Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations46109
Missing cells0
Missing cells (%)0.0%
Duplicate rows3498
Duplicate rows (%)7.6%
Total size in memory8.2 MiB
Average record size in memory187.6 B

Variable types

Numeric10
Categorical2

Alerts

Dataset has 3498 (7.6%) duplicate rowsDuplicates
Age is highly overall correlated with Gender and 1 other fieldsHigh correlation
Gender is highly overall correlated with Age and 3 other fieldsHigh correlation
Height_cm is highly overall correlated with Gender and 1 other fieldsHigh correlation
Subject is highly overall correlated with Age and 3 other fieldsHigh correlation
Weight_kg is highly overall correlated with Gender and 1 other fieldsHigh correlation
AccZ has 1496 (3.2%) zeros Zeros

Reproduction

Analysis started2025-06-06 06:43:17.299044
Analysis finished2025-06-06 06:43:23.928263
Duration6.63 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

SpO2
Real number (ℝ)

Distinct84
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.932588
Minimum80.000214
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-06-06T12:13:23.977260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum80.000214
5-th percentile93.000122
Q195
median96
Q396.999954
95-th percentile98
Maximum100
Range19.999786
Interquartile range (IQR)1.9999542

Descriptive statistics

Standard deviation1.7133757
Coefficient of variation (CV)0.017860205
Kurtosis15.134021
Mean95.932588
Median Absolute Deviation (MAD)1
Skewness-2.2212384
Sum4423355.7
Variance2.9356562
MonotonicityNot monotonic
2025-06-06T12:13:24.057260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 6313
 
13.7%
96 3613
 
7.8%
95.99993896 2890
 
6.3%
96 2751
 
6.0%
96.00001526 2223
 
4.8%
96.99995422 2169
 
4.7%
96.99996948 1946
 
4.2%
97.99993896 1676
 
3.6%
98 1647
 
3.6%
96.99987793 1558
 
3.4%
Other values (74) 19323
41.9%
ValueCountFrequency (%)
80.00021363 29
 
0.1%
81 5
 
< 0.1%
82.00006104 76
0.2%
83.00012207 24
 
0.1%
84.00018311 10
 
< 0.1%
85.00024415 20
 
< 0.1%
86.00003052 3
 
< 0.1%
87.00009156 9
 
< 0.1%
87.00010681 1
 
< 0.1%
88 15
 
< 0.1%
ValueCountFrequency (%)
100 20
 
< 0.1%
99.99989319 27
 
0.1%
99 23
 
< 0.1%
99 13
 
< 0.1%
99 700
1.5%
98.99998474 49
 
0.1%
98.99996948 398
 
0.9%
98.99990844 10
 
< 0.1%
98 1647
3.6%
97.99996948 1130
2.5%

HR
Real number (ℝ)

Distinct851
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.319531
Minimum48.000183
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-06-06T12:13:24.134260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum48.000183
5-th percentile57.000198
Q165.000641
median75.000381
Q387.000107
95-th percentile106.99966
Maximum134
Range85.999817
Interquartile range (IQR)21.999466

Descriptive statistics

Standard deviation15.300152
Coefficient of variation (CV)0.19788212
Kurtosis0.07148657
Mean77.319531
Median Absolute Deviation (MAD)10.000381
Skewness0.70058454
Sum3565126.3
Variance234.09466
MonotonicityNot monotonic
2025-06-06T12:13:24.203260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.00042726 355
 
0.8%
88 333
 
0.7%
59.0009003 316
 
0.7%
80 309
 
0.7%
54.00065615 304
 
0.7%
61 303
 
0.7%
84 290
 
0.6%
57.00019837 276
 
0.6%
58.0000763 270
 
0.6%
57.00044252 267
 
0.6%
Other values (841) 43086
93.4%
ValueCountFrequency (%)
48.00018311 3
 
< 0.1%
49.000412 19
 
< 0.1%
50.00003052 3
 
< 0.1%
50.00064089 7
 
< 0.1%
51.00086978 57
 
0.1%
51.00093081 6
 
< 0.1%
52.00019837 176
0.4%
52.00080874 1
 
< 0.1%
53 4
 
< 0.1%
53.00004578 5
 
< 0.1%
ValueCountFrequency (%)
134 15
< 0.1%
133 4
 
< 0.1%
132 8
< 0.1%
131 1
 
< 0.1%
130.9993439 1
 
< 0.1%
130 5
 
< 0.1%
129.9999695 3
 
< 0.1%
128.9996338 9
< 0.1%
128.9996033 1
 
< 0.1%
128.999588 3
 
< 0.1%

AccX
Real number (ℝ)

Distinct2660
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55123933
Minimum-1.319995
Maximum1.9099857
Zeros251
Zeros (%)0.5%
Negative5770
Negative (%)12.5%
Memory size360.4 KiB
2025-06-06T12:13:24.263772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.319995
5-th percentile-0.40997482
Q10.26002136
median0.61996887
Q30.94999847
95-th percentile1.12
Maximum1.9099857
Range3.2299806
Interquartile range (IQR)0.68997711

Descriptive statistics

Standard deviation0.47704864
Coefficient of variation (CV)0.86541112
Kurtosis0.014333985
Mean0.55123933
Median Absolute Deviation (MAD)0.34002747
Skewness-0.79982421
Sum25417.094
Variance0.22757541
MonotonicityNot monotonic
2025-06-06T12:13:24.332776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.069993591 379
 
0.8%
0.9899574267 342
 
0.7%
0.999978637 316
 
0.7%
1.160046999 314
 
0.7%
1.130004273 292
 
0.6%
1.059974364 288
 
0.6%
1.069954527 273
 
0.6%
0.9499984741 261
 
0.6%
0.81 256
 
0.6%
0.9999763482 255
 
0.6%
Other values (2650) 43133
93.5%
ValueCountFrequency (%)
-1.319994964 1
 
< 0.1%
-0.8499888607 1
 
< 0.1%
-0.8499763482 1
 
< 0.1%
-0.8299803156 10
 
< 0.1%
-0.8199971007 1
 
< 0.1%
-0.8199896237 1
 
< 0.1%
-0.819976043 28
0.1%
-0.819963683 1
 
< 0.1%
-0.8099765007 1
 
< 0.1%
-0.8099717704 43
0.1%
ValueCountFrequency (%)
1.909985656 1
< 0.1%
1.819968261 1
< 0.1%
1.73995941 1
< 0.1%
1.649986267 1
< 0.1%
1.639999847 1
< 0.1%
1.61998352 1
< 0.1%
1.619983215 1
< 0.1%
1.619964599 1
< 0.1%
1.539954833 1
< 0.1%
1.44995407 1
< 0.1%

AccY
Real number (ℝ)

Distinct1990
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.6920758
Minimum-2.0799742
Maximum0.83998993
Zeros26
Zeros (%)0.1%
Negative45612
Negative (%)98.9%
Memory size360.4 KiB
2025-06-06T12:13:24.398777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.0799742
5-th percentile-1.0399951
Q1-0.9500058
median-0.71004791
Q3-0.49002716
95-th percentile-0.24001877
Maximum0.83998993
Range2.9199641
Interquartile range (IQR)0.45997864

Descriptive statistics

Standard deviation0.27606725
Coefficient of variation (CV)-0.39889741
Kurtosis0.027447101
Mean-0.6920758
Median Absolute Deviation (MAD)0.23008789
Skewness0.55775886
Sum-31910.923
Variance0.076213125
MonotonicityNot monotonic
2025-06-06T12:13:24.467776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3699774163 491
 
1.1%
-0.4999743645 481
 
1.0%
-1.040023652 416
 
0.9%
-1.03998413 404
 
0.9%
-1.050037233 400
 
0.9%
-0.869978637 371
 
0.8%
-1.039995117 370
 
0.8%
-1.000054018 325
 
0.7%
-0.6100161748 313
 
0.7%
-1.029973449 294
 
0.6%
Other values (1980) 42244
91.6%
ValueCountFrequency (%)
-2.079974212 1
< 0.1%
-1.829927061 1
< 0.1%
-1.819960326 1
< 0.1%
-1.749990081 1
< 0.1%
-1.74994293 1
< 0.1%
-1.709963073 1
< 0.1%
-1.559976196 1
< 0.1%
-1.549952742 1
< 0.1%
-1.499967391 1
< 0.1%
-1.489967956 1
< 0.1%
ValueCountFrequency (%)
0.8399899289 1
< 0.1%
0.7499481185 1
< 0.1%
0.709963683 1
< 0.1%
0.699962157 1
< 0.1%
0.689958037 1
< 0.1%
0.6799792474 1
< 0.1%
0.6799751427 1
< 0.1%
0.6799673452 1
< 0.1%
0.6699766533 2
< 0.1%
0.6499588 1
< 0.1%

AccZ
Real number (ℝ)

Zeros 

Distinct2875
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.16086102
Minimum-1.789971
Maximum2.1499791
Zeros1496
Zeros (%)3.2%
Negative34620
Negative (%)75.1%
Memory size360.4 KiB
2025-06-06T12:13:24.534777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.789971
5-th percentile-0.9899855
Q1-0.23997436
median-0.10002625
Q3-0.010000916
95-th percentile0.42996582
Maximum2.1499791
Range3.9399501
Interquartile range (IQR)0.22997345

Descriptive statistics

Standard deviation0.3775992
Coefficient of variation (CV)-2.3473629
Kurtosis1.7921363
Mean-0.16086102
Median Absolute Deviation (MAD)0.11000946
Skewness-0.53835668
Sum-7417.1409
Variance0.14258115
MonotonicityNot monotonic
2025-06-06T12:13:24.610778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1496
 
3.2%
-0.09003570666 810
 
1.8%
0.1899530015 530
 
1.1%
-0.05003326518 514
 
1.1%
-0.2100415052 489
 
1.1%
-0.07003814814 469
 
1.0%
-0.1700219733 396
 
0.9%
-0.04998931852 384
 
0.8%
0.1099572741 371
 
0.8%
-0.210037843 368
 
0.8%
Other values (2865) 40282
87.4%
ValueCountFrequency (%)
-1.789971007 1
< 0.1%
-1.699978942 1
< 0.1%
-1.689991455 1
< 0.1%
-1.689965667 1
< 0.1%
-1.669965514 1
< 0.1%
-1.659973296 1
< 0.1%
-1.649987182 1
< 0.1%
-1.649984436 1
< 0.1%
-1.639979247 1
< 0.1%
-1.629980926 1
< 0.1%
ValueCountFrequency (%)
2.149979095 1
 
< 0.1%
1.389979553 1
 
< 0.1%
1.289976501 1
 
< 0.1%
1.12997055 1
 
< 0.1%
1.119953001 1
 
< 0.1%
1.109955138 1
 
< 0.1%
1.049967956 1
 
< 0.1%
1.019974364 1
 
< 0.1%
0.999978637 3
< 0.1%
0.9999694815 1
 
< 0.1%

Temp
Real number (ℝ)

Distinct509
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.909895
Minimum24.900037
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-06-06T12:13:24.681777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24.900037
5-th percentile29.100052
Q130.999983
median31.999939
Q333.099922
95-th percentile34.299916
Maximum36
Range11.099963
Interquartile range (IQR)2.099939

Descriptive statistics

Standard deviation1.5966781
Coefficient of variation (CV)0.050037083
Kurtosis0.81200123
Mean31.909895
Median Absolute Deviation (MAD)0.99996033
Skewness-0.51201325
Sum1471333.4
Variance2.5493809
MonotonicityNot monotonic
2025-06-06T12:13:24.754778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.1999649 803
 
1.7%
31.79997253 777
 
1.7%
34.09997101 774
 
1.7%
31.79999084 732
 
1.6%
31.99998779 713
 
1.5%
33.09997406 698
 
1.5%
30.9999237 643
 
1.4%
32.59995727 633
 
1.4%
33.29994812 589
 
1.3%
31.19992065 583
 
1.3%
Other values (499) 39164
84.9%
ValueCountFrequency (%)
24.90003662 6
 
< 0.1%
25.10009308 9
< 0.1%
25.30005036 9
< 0.1%
25.60007019 2
 
< 0.1%
25.60008545 9
< 0.1%
25.80004273 12
< 0.1%
25.80004273 9
< 0.1%
25.80006409 4
 
< 0.1%
26 16
< 0.1%
26.00001526 9
< 0.1%
ValueCountFrequency (%)
36 1
 
< 0.1%
35.79990539 42
 
0.1%
35.59994812 144
0.3%
35.39999084 273
0.6%
35.19997559 94
 
0.2%
35.19989624 324
0.7%
34.89998474 102
 
0.2%
34.89996033 73
 
0.2%
34.89988403 1
 
< 0.1%
34.69999084 123
 
0.3%

EDA
Real number (ℝ)

Distinct9730
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7459133
Minimum0.0020081179
Maximum9.1469242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-06-06T12:13:25.001777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0020081179
5-th percentile0.042002106
Q10.25400116
median0.80002435
Q32.4539658
95-th percentile6.7519533
Maximum9.1469242
Range9.144916
Interquartile range (IQR)2.1999646

Descriptive statistics

Standard deviation2.1249485
Coefficient of variation (CV)1.2170985
Kurtosis1.801029
Mean1.7459133
Median Absolute Deviation (MAD)0.68002138
Skewness1.6106512
Sum80502.318
Variance4.5154063
MonotonicityNot monotonic
2025-06-06T12:13:25.063779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03300051881 573
 
1.2%
0.03500100711 271
 
0.6%
0.07000056459 256
 
0.6%
0.04200210578 239
 
0.5%
0.08702182073 201
 
0.4%
0.02500088504 196
 
0.4%
0.2280192572 186
 
0.4%
0.02000024415 180
 
0.4%
0.07200024415 175
 
0.4%
0.0370003357 164
 
0.4%
Other values (9720) 43668
94.7%
ValueCountFrequency (%)
0.002008117924 1
 
< 0.1%
0.002026383251 1
 
< 0.1%
0.01007843257 1
 
< 0.1%
0.01400109867 7
 
< 0.1%
0.01600042726 52
 
0.1%
0.01800091556 22
 
< 0.1%
0.02000024415 180
0.4%
0.02200073244 116
0.3%
0.02200236518 1
 
< 0.1%
0.02400006104 64
 
0.1%
ValueCountFrequency (%)
9.146924162 1
 
< 0.1%
9.021957823 57
0.1%
9.010981658 60
0.1%
8.999915525 1
 
< 0.1%
8.910936613 71
0.2%
8.899960448 58
0.1%
8.887994629 51
0.1%
8.876928495 1
 
< 0.1%
8.80198468 4
 
< 0.1%
8.790918546 31
0.1%

Subject
Categorical

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Subject11
 
2532
Subject20
 
2521
Subject1
 
2293
Subject2
 
2291
Subject17
 
2283
Other values (15)
34189 

Length

Max length9
Median length9
Mean length8.5544688
Min length8

Characters and Unicode

Total characters394438
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSubject1
2nd rowSubject1
3rd rowSubject1
4th rowSubject1
5th rowSubject1

Common Values

ValueCountFrequency (%)
Subject11 2532
 
5.5%
Subject20 2521
 
5.5%
Subject1 2293
 
5.0%
Subject2 2291
 
5.0%
Subject17 2283
 
5.0%
Subject3 2282
 
4.9%
Subject18 2282
 
4.9%
Subject8 2282
 
4.9%
Subject4 2281
 
4.9%
Subject9 2281
 
4.9%
Other values (10) 22781
49.4%

Length

2025-06-06T12:13:25.123779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
subject11 2532
 
5.5%
subject20 2521
 
5.5%
subject1 2293
 
5.0%
subject2 2291
 
5.0%
subject17 2283
 
5.0%
subject3 2282
 
4.9%
subject18 2282
 
4.9%
subject8 2282
 
4.9%
subject4 2281
 
4.9%
subject9 2281
 
4.9%
Other values (10) 22781
49.4%

Most occurring characters

ValueCountFrequency (%)
S 46109
11.7%
u 46109
11.7%
b 46109
11.7%
j 46109
11.7%
e 46109
11.7%
c 46109
11.7%
t 46109
11.7%
1 27870
7.1%
2 7091
 
1.8%
0 4801
 
1.2%
Other values (7) 31913
8.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 276654
70.1%
Decimal Number 71675
 
18.2%
Uppercase Letter 46109
 
11.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 27870
38.9%
2 7091
 
9.9%
0 4801
 
6.7%
8 4564
 
6.4%
3 4562
 
6.4%
4 4560
 
6.4%
6 4559
 
6.4%
9 4558
 
6.4%
7 4555
 
6.4%
5 4555
 
6.4%
Lowercase Letter
ValueCountFrequency (%)
u 46109
16.7%
b 46109
16.7%
j 46109
16.7%
e 46109
16.7%
c 46109
16.7%
t 46109
16.7%
Uppercase Letter
ValueCountFrequency (%)
S 46109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 322763
81.8%
Common 71675
 
18.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 27870
38.9%
2 7091
 
9.9%
0 4801
 
6.7%
8 4564
 
6.4%
3 4562
 
6.4%
4 4560
 
6.4%
6 4559
 
6.4%
9 4558
 
6.4%
7 4555
 
6.4%
5 4555
 
6.4%
Latin
ValueCountFrequency (%)
S 46109
14.3%
u 46109
14.3%
b 46109
14.3%
j 46109
14.3%
e 46109
14.3%
c 46109
14.3%
t 46109
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 394438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 46109
11.7%
u 46109
11.7%
b 46109
11.7%
j 46109
11.7%
e 46109
11.7%
c 46109
11.7%
t 46109
11.7%
1 27870
7.1%
2 7091
 
1.8%
0 4801
 
1.2%
Other values (7) 31913
8.1%

Age
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.039884
Minimum19
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-06-06T12:13:25.165781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q123
median26
Q329
95-th percentile32
Maximum33
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7028979
Coefficient of variation (CV)0.14220101
Kurtosis-0.94489208
Mean26.039884
Median Absolute Deviation (MAD)3
Skewness0.16006315
Sum1200673
Variance13.711453
MonotonicityNot monotonic
2025-06-06T12:13:25.208779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
23 9119
19.8%
30 6854
14.9%
24 4800
10.4%
28 4573
9.9%
22 4558
9.9%
26 2532
 
5.5%
27 2282
 
4.9%
25 2281
 
4.9%
29 2280
 
4.9%
32 2279
 
4.9%
Other values (2) 4551
9.9%
ValueCountFrequency (%)
19 2279
 
4.9%
22 4558
9.9%
23 9119
19.8%
24 4800
10.4%
25 2281
 
4.9%
26 2532
 
5.5%
27 2282
 
4.9%
28 4573
9.9%
29 2280
 
4.9%
30 6854
14.9%
ValueCountFrequency (%)
33 2272
 
4.9%
32 2279
 
4.9%
30 6854
14.9%
29 2280
 
4.9%
28 4573
9.9%
27 2282
 
4.9%
26 2532
 
5.5%
25 2281
 
4.9%
24 4800
10.4%
23 9119
19.8%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
M
32198 
F
13911 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46109
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 32198
69.8%
F 13911
30.2%

Length

2025-06-06T12:13:25.261779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-06T12:13:25.293779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 32198
69.8%
f 13911
30.2%

Most occurring characters

ValueCountFrequency (%)
M 32198
69.8%
F 13911
30.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 46109
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 32198
69.8%
F 13911
30.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 46109
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 32198
69.8%
F 13911
30.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 32198
69.8%
F 13911
30.2%

Height_cm
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.59756
Minimum157
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-06-06T12:13:25.325777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum157
5-th percentile160
Q1165
median170
Q3177
95-th percentile182
Maximum182
Range25
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.6844548
Coefficient of variation (CV)0.045044341
Kurtosis-1.240184
Mean170.59756
Median Absolute Deviation (MAD)7
Skewness-0.040379141
Sum7866083
Variance59.050845
MonotonicityNot monotonic
2025-06-06T12:13:25.369986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
167 11401
24.7%
177 9138
19.8%
160 4800
10.4%
182 4563
9.9%
180 4559
 
9.9%
170 2532
 
5.5%
172 2291
 
5.0%
162 2279
 
4.9%
165 2274
 
4.9%
157 2272
 
4.9%
ValueCountFrequency (%)
157 2272
 
4.9%
160 4800
10.4%
162 2279
 
4.9%
165 2274
 
4.9%
167 11401
24.7%
170 2532
 
5.5%
172 2291
 
5.0%
177 9138
19.8%
180 4559
 
9.9%
182 4563
9.9%
ValueCountFrequency (%)
182 4563
9.9%
180 4559
 
9.9%
177 9138
19.8%
172 2291
 
5.0%
170 2532
 
5.5%
167 11401
24.7%
165 2274
 
4.9%
162 2279
 
4.9%
160 4800
10.4%
157 2272
 
4.9%

Weight_kg
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.017263
Minimum44
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.4 KiB
2025-06-06T12:13:25.415985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile44
Q157
median64
Q371
95-th percentile91
Maximum94
Range50
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.373619
Coefficient of variation (CV)0.20257761
Kurtosis-0.2291776
Mean66.017263
Median Absolute Deviation (MAD)7
Skewness0.70217189
Sum3043990
Variance178.8537
MonotonicityNot monotonic
2025-06-06T12:13:25.467988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
64 13675
29.7%
68 4572
 
9.9%
58 4561
 
9.9%
71 2532
 
5.5%
44 2521
 
5.5%
94 2293
 
5.0%
57 2283
 
5.0%
91 2282
 
4.9%
82 2281
 
4.9%
54 2279
 
4.9%
Other values (3) 6830
14.8%
ValueCountFrequency (%)
44 2521
 
5.5%
50 2279
 
4.9%
53 2279
 
4.9%
54 2279
 
4.9%
57 2283
 
5.0%
58 4561
 
9.9%
64 13675
29.7%
68 4572
 
9.9%
71 2532
 
5.5%
82 2281
 
4.9%
ValueCountFrequency (%)
94 2293
 
5.0%
91 2282
 
4.9%
90 2272
 
4.9%
82 2281
 
4.9%
71 2532
 
5.5%
68 4572
 
9.9%
64 13675
29.7%
58 4561
 
9.9%
57 2283
 
5.0%
54 2279
 
4.9%

Interactions

2025-06-06T12:13:23.163514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:17.881229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.503468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.046622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.620111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.174991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.758750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.492257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.998256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.577967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:23.226514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:17.945743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.562467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.110624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.677113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.243230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.810749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.545258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.059688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.635968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:23.278514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.001742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.624471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.166140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.731615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.301243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.862748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.598256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.115949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.684966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:23.336511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.063745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.675469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.223141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.788614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.359230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.916748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.649255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.177948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.743968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:23.395751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.125685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.732603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.279140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.840616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.417233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.973749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.701256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.245946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.800967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:23.453749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.186683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.787604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.339140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.898616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.473748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.029749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.752265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.305124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.861969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:23.508751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.247692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.837602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.400593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.956616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.528748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.077747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.800256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.359123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.924514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:23.560750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.308030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.889603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.453592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.010991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.579748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.126747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.847256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.414123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.977515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:23.614747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.372029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.943470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.510593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.066993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.644747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.177749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.900253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.468122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:23.057514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:23.671749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.435029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:18.994634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:19.565113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.118991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:20.702748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.230749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:21.949255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:22.527121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-06T12:13:23.106522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-06T12:13:25.513986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AccXAccYAccZAgeEDAGenderHRHeight_cmSpO2SubjectTempWeight_kg
AccX1.0000.487-0.161-0.0500.1400.2520.1260.120-0.0710.317-0.0290.066
AccY0.4871.000-0.284-0.0240.0600.2640.2970.224-0.0960.229-0.0750.052
AccZ-0.161-0.2841.000-0.1870.0260.064-0.436-0.0720.0040.264-0.050-0.242
Age-0.050-0.024-0.1871.0000.2020.8010.3490.0700.1251.0000.0950.456
EDA0.1400.0600.0260.2021.0000.2680.1040.027-0.1170.4510.3350.138
Gender0.2520.2640.0640.8010.2681.0000.1980.8290.1951.0000.1710.579
HR0.1260.297-0.4360.3490.1040.1981.0000.299-0.1330.3510.0060.239
Height_cm0.1200.224-0.0720.0700.0270.8290.2991.000-0.0721.0000.0310.416
SpO2-0.071-0.0960.0040.125-0.1170.195-0.133-0.0721.0000.303-0.0730.015
Subject0.3170.2290.2641.0000.4511.0000.3511.0000.3031.0000.3981.000
Temp-0.029-0.075-0.0500.0950.3350.1710.0060.031-0.0730.3981.000-0.004
Weight_kg0.0660.052-0.2420.4560.1380.5790.2390.4160.0151.000-0.0041.000

Missing values

2025-06-06T12:13:23.757260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-06T12:13:23.847263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SpO2HRAccXAccYAccZTempEDASubjectAgeGenderHeight_cmWeight_kg
096.99996989.0007630.760015-0.869990-0.10000430.1000430.083039Subject130M17794
196.99996988.0001370.750005-0.869990-0.11003130.1000430.081028Subject130M17794
296.99996987.0004430.750005-0.869990-0.11003130.1000430.083039Subject130M17794
396.99996987.0004430.750005-0.879993-0.10000430.1000430.083039Subject130M17794
496.99996987.0004430.760015-0.859987-0.10000430.1000430.081028Subject130M17794
596.00000087.0004430.750005-0.869990-0.10000430.1000430.081028Subject130M17794
696.00000086.0007480.750005-0.879993-0.11003130.1000430.083039Subject130M17794
796.00000085.0001220.750005-0.869990-0.10000430.1000430.083039Subject130M17794
896.00000085.0001220.740045-0.879993-0.10000430.1000430.081028Subject130M17794
996.00000082.0001070.740045-0.879993-0.10000430.1000430.083039Subject130M17794
SpO2HRAccXAccYAccZTempEDASubjectAgeGenderHeight_cmWeight_kg
4609997.99993965.0002140.8799940.3499840.55996432.3999450.377027Subject2024F16044
4610097.99993963.0008850.679983-1.2899910.12999732.3999450.419028Subject2024F16044
4610197.99993962.0000610.329951-0.1100310.55996432.3999450.416021Subject2024F16044
4610297.99993962.0000610.879994-0.0400430.61999232.3999450.389004Subject2024F16044
4610397.99993963.0008850.739959-0.2800390.75997232.3999450.385012Subject2024F16044
4610497.99993965.0002140.579951-0.7999880.02999332.3999450.416021Subject2024F16044
4610597.99993967.0007020.519974-0.9499750.08997832.3999450.422036Subject2024F16044
4610697.99993968.0003660.569964-0.9699960.08997832.3999450.428052Subject2024F16044
4610797.99993971.0005190.539949-0.9599850.08997832.3999450.425044Subject2024F16044
4610897.99993971.0005190.529962-0.9599850.10998732.1999880.431005Subject2024F16044

Duplicate rows

Most frequently occurring

SpO2HRAccXAccYAccZTempEDASubjectAgeGenderHeight_cmWeight_kg# duplicates
255596.99995458.0000760.810000-0.779980-0.31002034.4999970.072000Subject1922M1676416
211796.00001556.0003200.999962-0.590020-0.23001933.4998960.081000Subject1922M1676415
32494.00006170.0006560.560007-0.969998-0.05003333.2999182.908971Subject1126M1707114
207396.00001554.0005650.999962-0.590020-0.23001933.8998840.081000Subject1922M1676414
224096.00001560.0008550.869986-0.759984-0.20002531.9999420.070001Subject1922M1676413
265096.99995462.0006870.369955-1.029973-0.05003630.5999690.031000Subject1823M1776413
57594.99996961.0004581.159968-0.260010-0.11001731.2000007.008921Subject328M1779112
222996.00001560.0002290.359956-1.029973-0.05003630.9999630.033001Subject1823M1776412
252296.99995457.0001980.810000-0.779980-0.31002034.4999970.072000Subject1922M1676412
11192.99996968.000000-0.369989-0.8600280.14997431.7999910.240029Subject1419F1605011